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Record Nr. |
UNINA9910484861903321 |
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Titolo |
Multimedia Big Data Computing for IoT Applications : Concepts, Paradigms and Solutions / / edited by Sudeep Tanwar, Sudhanshu Tyagi, Neeraj Kumar |
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Pubbl/distr/stampa |
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Singapore : , : Springer Singapore : , : Imprint : Springer, , 2020 |
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ISBN |
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Edizione |
[1st ed. 2020.] |
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Descrizione fisica |
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1 online resource (xiv, 477 pages) |
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Collana |
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Intelligent Systems Reference Library, , 1868-4394 ; ; 163 |
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Disciplina |
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Soggetti |
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Computational intelligence |
Computer security |
Big data |
Multimedia information systems |
Application software |
Computational Intelligence |
Systems and Data Security |
Big Data |
Multimedia Information Systems |
Information Systems Applications (incl. Internet) |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di contenuto |
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Multimedia Big data computing for IoT -- Energy Conservation in MMBD Computing and IoT – A Challenge -- An Architecture for the Real-Time Data Stream Monitoring in IoT -- Deep learning for Multimedia data in IoT -- Random Forest based Sarcastic Tweet Classification using multiple feature Collection -- Peak Average Power Ratio reduction in FBMC using SLM & PTS techniques -- Intelligent Personality Analysis on Indicators in IoT-MMBD Enabled Environment -- Data Reduction in MMBD Computing -- Large Scale MMBD Management and Retrieval -- Data Reduction Technique for Capsule Endoscopy -- Multimedia Social Big Data: Mining -- Advertisement prediction in social media environment using big data framework. |
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Sommario/riassunto |
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This book considers all aspects of managing the complexity of |
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Multimedia Big Data Computing (MMBD) for IoT applications and develops a comprehensive taxonomy. It also discusses a process model that addresses a number of research challenges associated with MMBD, such as scalability, accessibility, reliability, heterogeneity, and Quality of Service (QoS) requirements, presenting case studies to demonstrate its application. Further, the book examines the layered architecture of MMBD computing and compares the life cycle of both big data and MMBD. Written by leading experts, it also includes numerous solved examples, technical descriptions, scenarios, procedures, and algorithms. |
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